Action recognition research historically has focused on increasing accuracy on datasets in highly controlled environments. Perfect or near perfect offline action recognition accuracy on scripted datasets has been achieved. The aim of this thesis is to deal with the more complex problem of online action recognition with low latency in real world scenarios. To fulfil this aim two new multi-modal gaming datasets were captured and three novel algorithms for online action recognition were proposed. Two new gaming datasets, G3D and G3Di for real-time action recognition with multiple actions and multi-modal data were captured and publicly released. Furthermore, G3Di was captured using a novel game-sourcing method so the actions are realistic. Three novel algorithms for online action recognition with low latency were proposed. Firstly, Dynamic Feature Selection, which combines the discriminative power of Random Forests for feature selection with an ensemble of AdaBoost classifiers for dynamic classification. Secondly, Clustered Spatio-Temporal Manifolds, which modelled the dynamics of human actions with style invariant action templates that were combined with Dynamic Time Warping for execution rate invariance. Finally, a Hierarchical Transfer Learning framework, comprised of a novel transfer learning algorithm to detect compound actions in addition to hierarchical interaction detection to recognise the actions and interactions of multiple subjects. The proposed algorithms run in real-time with low latency ensuring they are suitable for a wide range of natural user interface applications including gaming. State-of-the art results were achieved for online action recognition. Experimental results indicate higher complexity of the G3Di dataset in comparison to the existing gaming datasets, highlighting the importance of this dataset for designing algorithms suitable for realistic interactive applications. This thesis has advanced the study of realistic action recognition and is expected to serve as a basis for further study within the research community.